10 citations · 16 across the 11 of their papers we have counts for
11 papers
PHASE: encoding global protein ensembles with local Hamiltonians and all-atom backmapping
Daniele Angioletti, Marco Nobile, Matteo Carli +1
Protein function is governed by conformational ensembles, which can be viewed as high-dimensional probability distributions over molecular conformations. Yet the statistical organi…
GEqTrain: A Configuration-Driven Framework for Retargeting Equivariant Graph Neural Networks Across 3D Scientific Tasks
Daniele Angioletti, Marco Nobile, Vittorio Limongelli
Equivariant graph neural networks provide a powerful modeling language for three-dimensional scientific data, but their reuse is often limited by implementations tied to specific t…
Salp Swarm Optimization: a Critical Review
Mauro Castelli, Luca Manzoni, Luca Mariot +2
In the crowded environment of bio-inspired population-based metaheuristics, the Salp Swarm Optimization (SSO) algorithm recently appeared and immediately gained a lot of momentum.…
USE-Net: incorporating Squeeze-and-Excitation blocks into U-Net for prostate zonal segmentation of multi-institutional MRI datasets
Leonardo Rundo, Changhee Han, Yudai Nagano +12
Prostate cancer is the most common malignant tumors in men but prostate Magnetic Resonance Imaging (MRI) analysis remains challenging. Besides whole prostate gland segmentation, th…
CNN-based Prostate Zonal Segmentation on T2-weighted MR Images: A Cross-dataset Study
Leonardo Rundo, Changhee Han, Jin Zhang +10
Prostate cancer is the most common cancer among US men. However, prostate imaging is still challenging despite the advances in multi-parametric Magnetic Resonance Imaging (MRI), wh…
GenHap: A Novel Computational Method Based on Genetic Algorithms for Haplotype Assembly
Andrea Tangherloni, Simone Spolaor, Leonardo Rundo +6
The computational problem of inferring the full haplotype of a cell starting from read sequencing data is known as haplotype assembly, and consists in assigning all heterozygous Si…
Investigating the performance of multi-objective optimization when learning Bayesian Networks
Paolo Cazzaniga, Marco S. Nobile, Daniele Ramazzotti
Bayesian Networks have been widely used in the last decades in many fields, to describe statistical dependencies among random variables. In general, learning the structure of such…
Parallel Implementation of Efficient Search Schemes for the Inference of Cancer Progression Models
Daniele Ramazzotti, Marco S. Nobile, Paolo Cazzaniga +2
The emergence and development of cancer is a consequence of the accumulation over time of genomic mutations involving a specific set of genes, which provides the cancer clones with…
Efficient computational strategies to learn the structure of probabilistic graphical models of cumulative phenomena
Daniele Ramazzotti, Marco S. Nobile, Marco Antoniotti +1
Structural learning of Bayesian Networks (BNs) is a NP-hard problem, which is further complicated by many theoretical issues, such as the I-equivalence among different structures.…
GPU-powered Simulation Methodologies for Biological Systems
Daniela Besozzi, Giulio Caravagna, Paolo Cazzaniga +3
The study of biological systems witnessed a pervasive cross-fertilization between experimental investigation and computational methods. This gave rise to the development of new met…
GEOS RR Lyr Survey: Blazhko Period Measurement of Three RRab Stars - CX Lyrae, NU Aurigae and VY Corona Borealis
Pierre de Ponthiere, Jean-Francois Le Borgne, F. Fumagalli +6
We present the results of collaborative observations of three RR Lyrae stars (CX Lyr, NU Aur and VY CrB) which have a strong Blazhko effect. This work has been initiated and perfor…